Automation Bias in AI-Decision Support: Results from an Empirical Study.

Introduction: Automation bias poses a significant challenge to the effectiveness of Clinical Decision Support Systems (CDSS), potentially compromising diagnostic accuracy. Previous research highlights trust, self-confidence, and task difficulty as key determinants. With the increasing availability of AI-enabled CDSS, automation bias attains new attention.

Macrophage-Derived Factors with the Potential to Contribute to Pathogenicity of HIV-1 and HIV-2: Role of CCL-2/MCP-1.

Human immunodeficiency virus type 2 (HIV-2) is known to be less pathogenic than HIV-1. However, the mechanism(s) underlying the decreased HIV-2 pathogenicity is not fully understood. Herein, we report that β-chemokine CCL2 expression was increased in HIV-1-infected human monocyte-derived macrophages (MDM) but decreased in HIV-2-infected MDM when compared to uninfected MDM.

Trust in Digitalization and AI: Findings from a Qualitative Study on Healthcare Professionals in Germany.

The acceptance and use of digital technologies depend on the trustworthiness attributed to them. Experts were interviewed about how they assign trust to digital technologies or AI (N=12). The data were analyzed applying the focused qualitative content analysis.

The Representation of Trust in Artificial Intelligence Healthcare Research.

Artificial intelligence (AI) tends to emerge as a relevant component of medical care, previously reserved for medical experts. A key factor for the utilization of AI is the user's trust in the AI itself, respectively the AIt's decision process, but AI-models are lacking information about this process, the so-called Black Box, potentially affecting usert's trust in AI. This analysis' objective is the description of trust-related research regarding AI-models and the relevance of trust in comparison to other AI-related research topics in healthcare.

Patient Safety Informatics: Criteria Development for Assessing the Maturity of Digital Patient Safety in Hospitals.

With the start of the 21st century, patient safety as a topic of special interest has attracted increasing attention in both academia and clinical practice. As technology has continued to develop since then, questions and focal points surrounding the topic have also shifted. In particular, questions regarding the impact of digitalization on patient safety and its measurement are now of high interest.